论文标题
团队渠道 - 萨克（Slam-Slam）：一种合作映射的车辆定位方法
Team Channel-SLAM: A Cooperative Mapping Approach to Vehicle Localization
论文作者
论文摘要
车辆定位被认为是自动驾驶系统中的关键要素。尽管常规定位需要使用网络基础设施中的GPS和/或Beacon信号进行三角测量，但它们对多路径和信号阻塞敏感。但是，诸如渠道 - 林方法之类的最新建议表明，原则上有可能利用多路径来改善单个车辆的定位。在本文中，我们得出了一个合作的渠道 - 萨克框架，该框架称为团队频道 - 萨克。与以前的工作不同，团队频道 - 萨拉姆不仅利用反映接收器周围物体的固定性质来表征单个车辆的位置，还可以通过多路径信号来表征单个车辆的位置，还可以利用道路交通的多车辆方面以进一步改善位置。特别是，团队渠道渠道slam在多个相邻车辆上的反射器之间的相关性在多个相邻的车辆之间实现多辆车，以实现多辆车的位置。我们的方法使用亲和力传播聚类和合作粒子滤波器。新框架被证明可以在单车定位情况下进行大量改进。
Vehicle positioning is considered a key element in autonomous driving systems. While conventional positioning requires the use of GPS and/or beacon signals from network infrastructure for triangulation, they are sensitive to multi-path and signal obstruction. However, recent proposals like the Channel-SLAM method showed it was possible in principle to in fact leverage multi-path to improve positioning of a single vehicle. In this paper, we derive a cooperative Channel-SLAM framework, which is referred as Team Channel-SLAM. Different from the previous work, Team Channel-SLAM not only exploits the stationary nature of reflecting objects around the receiver to characterize the location of a single vehicle through multi-path signals, but also capitalizes on the multi-vehicle aspects of road traffic to further improve positioning.Specifically, Team Channel-SLAM exploits the correlation between reflectors around multiple neighboring vehicles to achieve high precision multiple vehicle positioning. Our method uses affinity propagation clustering and cooperative particle filter. The new framework is shown to give substantial improvement over the single vehicle positioning situation.
